Shaoqian_Chen_599Final

library(dplyr)       # for data manipulation

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)     # for data visualization
library(stringr)     # for string functionality
library(cluster)     # for general clustering algorithms
library(factoextra)  # for visualizing cluster results

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library(yardstick)

## For binary classification, the first factor level is assumed to be the event.
## Set the global option `yardstick.event_first` to `FALSE` to change this.

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:dplyr':
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##     intersect, setdiff, union

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(ggplot2)
library(ggmap)

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

library(dplyr)
library(readr)

## 
## Attaching package: 'readr'

## The following object is masked from 'package:yardstick':
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##     spec

ggmap::register_google(key = "AIzaSyDjdKw8npxWHoH5eL85_OrfxTUSyf8Wxcg")

Question 1

a)

mnist <- dslabs::read_mnist()

set.seed(1)
sample_1 = sample(60000,10000)
features <- mnist$train$images[sample_1,]

# Use k-means model with 10 centers and 10 random starts
mnist_clustering <- kmeans(features, centers = 10, nstart = 10)

## Warning: did not converge in 10 iterations

## Warning: did not converge in 10 iterations

## Warning: did not converge in 10 iterations

## Warning: did not converge in 10 iterations

# Print contents of the model output
str(mnist_clustering)

## List of 9
##  $ cluster     : int [1:10000] 7 7 6 9 1 10 5 9 5 10 ...
##  $ centers     : num [1:10, 1:784] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:10] "1" "2" "3" "4" ...
##   .. ..$ : NULL
##  $ totss       : num 3.42e+10
##  $ withinss    : num [1:10] 1.02e+09 2.25e+09 2.42e+09 2.59e+09 3.70e+09 ...
##  $ tot.withinss: num 2.54e+10
##  $ betweenss   : num 8.78e+09
##  $ size        : int [1:10] 734 810 759 914 1246 1043 1408 703 938 1445
##  $ iter        : int 11
##  $ ifault      : int 2
##  - attr(*, "class")= chr "kmeans"

# Extract cluster centers
mnist_centers <- mnist_clustering$centers
# Create mode function
mode_fun <- function(x){ which.max(tabulate(x))
}
mnist_comparison <- data.frame(
  cluster = mnist_clustering$cluster,
  actual = mnist$train$labels[sample_1]
) %>%
  group_by(cluster) %>%
  mutate(mode = mode_fun(actual)) %>%
  ungroup() %>%
  mutate_all(factor, levels = 0:9)
# Create confusion matrix and plot results
yardstick::conf_mat(
  mnist_comparison,
  truth = actual,
  estimate = mode
) %>%
  autoplot(type = 'heatmap')

b)

m1 = prcomp(features, scale = F)

newdata = m1$x
actual = mnist$train$labels[sample_1]
ggplot(data.frame(newdata),aes(x=PC1,y=PC2))+geom_text(aes(label=actual,color = factor(actual)),alpha=0.8)

## From the plot above we can identidy that digit 0 are well seperated and digit 1 are not

Questioin 2

a)

df = crime
#head(df)

dfselected <- df$offense=="robbery"|df$offense=="aggravated assault"|df$offense=="rape"|df$offense=="murder"
df2 <- df[dfselected,]

geocode("Houston")

## Source : https://maps.googleapis.com/maps/api/geocode/json?address=Houston&key=xxx

## # A tibble: 1 x 2
##     lon   lat
##   <dbl> <dbl>
## 1 -95.4  29.8

Houston.map = get_googlemap(center=c(lon = -95.369345,lat = 29.760155),lon=c(-95.39681,-95.34188),lat = c(29.73631,29.78400),zoom = calc_zoom(c(-95.39681,-95.34188),c(29.73631,29.78400)), scale = 2)

## Source : https://maps.googleapis.com/maps/api/staticmap?center=29.760155,-95.369345&zoom=14&size=640x640&scale=2&maptype=terrain&key=xxx

p = ggmap(Houston.map)

p+ geom_point(data = df2, aes(x=lon,y=lat,colour = factor(offense)),size = 1,alpha=1,na.rm = TRUE)

b)

p+ geom_point(data = df2, aes(x=lon,y=lat,colour = factor(offense)),size = 1,alpha=0.8,na.rm = TRUE)+facet_wrap(~offense,nrow = 2)

Question 3

data = read_csv("question3.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   X1 = col_double(),
##   FIPS = col_double(),
##   County = col_character(),
##   population = col_double(),
##   cases = col_double(),
##   lat = col_double(),
##   lon = col_double()
## )

data$log <- log10(data$cases)
head(data)

## # A tibble: 6 x 8
##      X1  FIPS County            population cases   lat   lon   log
##   <dbl> <dbl> <chr>                  <dbl> <dbl> <dbl> <dbl> <dbl>
## 1     1 10001 Kent County           180786   503  39.1 -75.5  2.70
## 2     2 10003 New Castle County     558753  1352  39.6 -75.6  3.13
## 3     3 10005 Sussex County         234225  1317  38.7 -75.3  3.12
## 4     4  1001 Autauga County         55869    32  32.5 -86.6  1.51
## 5     5  1003 Baldwin County        223234   132  30.7 -87.7  2.12
## 6     6  1005 Barbour County         24686    29  31.9 -85.4  1.46

us <- c(left = -125, bottom = 25.75, right = -67, top = 49)
US.map = get_stamenmap(us, zoom = 5, maptype = "toner-lite")

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p = ggmap(US.map)
p+ geom_point(data=data,aes(x=lon,y=lat),colour = "#b207f0",size = data$log,alpha=0.3,na.rm = TRUE)